Volume 27 , Issue 1 , PP: 73-84, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ammar Kuti Nasser 1 *
Doi: https://doi.org/10.54216/IJNS.270107
Predicting future energy consumption plays a vital role in maximizing resource utilization, reducing costs, and enhancing sustainability. Researchers employ advanced statistical and machine learning models to improve the accuracy of time series forecasting. Real-world energy consumption data is analyzed using State-Space Models (SSMs), Vector Auto Regression (VAR), Structural VAR (SVAR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), and Bayesian Structural Time Series (BSTS). An evaluation of Long Short-Term Memory (LSTM) networks and the Prophet model is conducted alongside a comparison with the aforementioned models. The proposed method integrates neutrosophic statistical models for feature extraction and residual analysis, generating outputs suitable for machine learning processing. The results indicate that incorporating judgment-based neutrosophic statistical approaches with AI-driven neutrosophic prediction models yields superior forecasts of power consumption, contributing to more comprehensive and effective energy usage prediction methodologies.
Neutrosophic logic , Neutrosophic model , Bayesian Structural Time Series , Energy Consumption Forecasting , Hybrid Model , Machine Learning , State-Space Models
[1] S. Baratsas, F. Iseri, and E. N. Pistikopoulos, “A hybrid statistical and machine learning based forecasting framework for the energy sector,” Computers & Chemical Engineering, vol. 188, p. 108740, 2024.
[2] A. Alsulaili, N. Aboramyah, N. Alenezi, and M. Alkhalidi, “Advancing electricity consumption forecasts in arid climates through machine learning and statistical approaches,” Sustainability, vol. 16, no. 15, p. 6326, 2024.
[3] H. Alizadegan, B. Rashidi Malki, A. Radmehr, H. Karimi, and M. A. Ilani, “Comparative study of long short-term memory (LSTM), bidirectional LSTM, and traditional machine learning approaches for energy consumption prediction,” Energy Exploration & Exploitation, vol. 43, no. 1, pp. 281–301, 2025.
[4] I. Amalou, N. Mouhni, and A. Abdali, “Multivariate time series prediction by RNN architectures for energy consumption forecasting,” Energy Reports, vol. 8, pp. 1084–1091, 2022.
[5] N. Andriopoulos et al., “Short term electric load forecasting based on data transformation and statistical machine learning,” Applied Sciences, vol. 11, no. 1, p. 158, 2020.
[6] J.-S. Chou and D.-S. Tran, “Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,” Energy, vol. 165, pp. 709–726, 2018.
[7] J. A. Mariño, M. E. Arrieta-Prieto, and S. A. Calderón V, “Comparison between statistical models and machine learning for forecasting multivariate time series: An empirical approach,” Communications in Statistics: Case Studies, Data Analysis and Applications, vol. 11, no. 1, pp. 56–91, 2025.
[8] P. Pełka, “Analysis and forecasting of monthly electricity demand time series using pattern-based statistical methods,” Energies, vol. 16, no. 2, p. 827, 2023.
[9] X. Li and X. Zhang, “A comparative study of statistical and machine learning models on carbon dioxide emissions prediction of China,” Environmental Science and Pollution Research, vol. 30, no. 55, pp. 117485–117502, 2023.
[10] M. Khalil, A. S. McGough, Z. Pourmirza, M. Pazhoohesh, and S. Walker, “Machine learning, deep learning and statistical analysis for forecasting building energy consumption—A systematic review,” Engineering Applications of Artificial Intelligence, vol. 115, p. 105287, 2022.
[11] A. M. N. C. Ribeiro, P. R. X. do Carmo, I. R. Rodrigues, D. Sadok, T. Lynn, and P. T. Endo, “Short-term firm-level energy-consumption forecasting for energy-intensive manufacturing: A comparison of machine learning and deep learning models,” Algorithms, vol. 13, no. 11, p. 274, 2020.
[12] N. Bhoj and R. S. Bhadoria, “Time-series based prediction for energy consumption of smart home data using hybrid convolution-recurrent neural network,” Telematics and Informatics, vol. 75, p. 101907, 2022.
[13] L. Cascone, S. Sadiq, S. Ullah, S. Mirjalili, H. U. R. Siddiqui, and M. Umer, “Predicting household electric power consumption using multi-step time series with convolutional LSTM,” Big Data Research, vol. 31, p. 100360, 2023.
[14] L. Charfeddine, E. Zaidan, A. Q. Alban, H. Bennasr, and A. Abulibdeh, “Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models,” Sustainable Cities and Society, vol. 98, p. 104860, 2023.
[15] M. H. L. Lee et al., “A comparative study of forecasting electricity consumption using machine learning models,” Mathematics, vol. 10, no. 8, p. 1329, 2022.
[16] R. Godahewa, C. Deng, A. Prouzeau, and C. Bergmeir, “A generative deep learning framework across time series to optimize the energy consumption of air conditioning systems,” IEEE Access, vol. 10, pp. 6842–6855, 2022.
[17] J. Chou and D. Truong, “Multistep energy consumption forecasting by metaheuristic optimization of time‐series analysis and machine learning,” International Journal of Energy Research, vol. 45, no. 3, pp. 4581–4612, 2021.
[18] A. Malki, E.-S. Atlam, and I. Gad, “Machine learning approach of detecting anomalies and forecasting time-series of IoT devices,” Alexandria Engineering Journal, vol. 61, no. 11, pp. 8973–8986, 2022.
[19] S. Bourhnane, M. R. Abid, R. Lghoul, K. Zine-Dine, N. Elkamoun, and D. Benhaddou, “Machine learning for energy consumption prediction and scheduling in smart buildings,” SN Applied Sciences, vol. 2, no. 2, p. 297, 2020.
[20] D. Peteleaza et al., “Electricity consumption forecasting for sustainable smart cities using machine learning methods,” Internet of Things, vol. 27, p. 101322, 2024.
[21] M. K. M. Shapi, N. A. Ramli, and L. J. Awalin, “Energy consumption prediction by using machine learning for smart building: Case study in Malaysia,” Developments in the Built Environment, vol. 5, p. 100037, 2021.
[22] M. Yucesan, E. Pekel, E. Celik, M. Gul, and F. Serin, “Forecasting daily natural gas consumption with regression, time series and machine learning based methods,” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 47, no. 1, pp. 4605–4620, 2025.
[23] S. Reddy, S. Akashdeep, R. Harshvardhan, and S. Kamath, “Stacking deep learning and machine learning models for short-term energy consumption forecasting,” Advanced Engineering Informatics, vol. 52, p. 101542, 2022.
[24] S. Seyedzadeh, F. P. Rahimian, I. Glesk, and M. Roper, “Machine learning for estimation of building energy consumption and performance: A review,” Visualization in Engineering, vol. 6, pp. 1–20, 2018.
[25] U.S. Energy Information Administration (EIA), “U.S. Energy Information Administration (EIA),” 2024. [Online]. Available: https://www.eia.gov/opendata.
[26] F. Smarandache, A Unifying Field in Logics: Neutrosophic Logic, Neutrosophy, Neutrosophic Set, Neutrosophic Probability, American Research Press, Rehoboth, 2003.
[27] M. Abobala, “On refined neutrosophic matrices and their applications in refined neutrosophic algebraic equations,” Journal of Mathematics, Hindawi, 2021.
[28] A. Polymenis, “A neutrosophic Student’s t-type of statistic for AR (1) random processes,” Journal of Fuzzy Extension and Applications, vol. 2, no. 4, pp. 388-393, 2021. doi: 10.22105/jfea.2021.287294.1149.
